Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio
Despite their great success in recent years, neural networks have been found to be vulnerable to adversarial attacks. These attacks are often based on slight perturbations of given inputs that cause them to be misclassified. Several methods have been proposed to formally prove robustness of a given...
Uložené v:
| Vydané v: | Machine learning Ročník 111; číslo 12; s. 4565 - 4584 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
Springer US
01.12.2022
Springer Nature B.V |
| Predmet: | |
| ISSN: | 0885-6125, 1573-0565 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Despite their great success in recent years, neural networks have been found to be vulnerable to adversarial attacks. These attacks are often based on slight perturbations of given inputs that cause them to be misclassified. Several methods have been proposed to formally prove robustness of a given network against such attacks. However, these methods typically give rise to high computational demands, which severely limit their scalability. Recent state-of-the-art approaches state the verification task as a minimisation problem, which is formulated and solved as a mixed-integer linear programming (MIP) problem. We extend this approach by leveraging automated algorithm configuration techniques and, more specifically, construct a portfolio of MIP solver configurations optimised for the neural network verification task. We test this approach on two recent, state-of-the-art MIP-based verification engines,
MIPVerify
and
Venus
, and achieve substantial improvements in CPU time by average factors of up to 4.7 and 10.3, respectively. |
|---|---|
| AbstractList | Despite their great success in recent years, neural networks have been found to be vulnerable to adversarial attacks. These attacks are often based on slight perturbations of given inputs that cause them to be misclassified. Several methods have been proposed to formally prove robustness of a given network against such attacks. However, these methods typically give rise to high computational demands, which severely limit their scalability. Recent state-of-the-art approaches state the verification task as a minimisation problem, which is formulated and solved as a mixed-integer linear programming (MIP) problem. We extend this approach by leveraging automated algorithm configuration techniques and, more specifically, construct a portfolio of MIP solver configurations optimised for the neural network verification task. We test this approach on two recent, state-of-the-art MIP-based verification engines,
$$\mathrm {MIPVerify}$$
MIPVerify
and
$$\mathrm {Venus}$$
Venus
, and achieve substantial improvements in CPU time by average factors of up to 4.7 and 10.3, respectively. Despite their great success in recent years, neural networks have been found to be vulnerable to adversarial attacks. These attacks are often based on slight perturbations of given inputs that cause them to be misclassified. Several methods have been proposed to formally prove robustness of a given network against such attacks. However, these methods typically give rise to high computational demands, which severely limit their scalability. Recent state-of-the-art approaches state the verification task as a minimisation problem, which is formulated and solved as a mixed-integer linear programming (MIP) problem. We extend this approach by leveraging automated algorithm configuration techniques and, more specifically, construct a portfolio of MIP solver configurations optimised for the neural network verification task. We test this approach on two recent, state-of-the-art MIP-based verification engines, MIPVerify and Venus , and achieve substantial improvements in CPU time by average factors of up to 4.7 and 10.3, respectively. Despite their great success in recent years, neural networks have been found to be vulnerable to adversarial attacks. These attacks are often based on slight perturbations of given inputs that cause them to be misclassified. Several methods have been proposed to formally prove robustness of a given network against such attacks. However, these methods typically give rise to high computational demands, which severely limit their scalability. Recent state-of-the-art approaches state the verification task as a minimisation problem, which is formulated and solved as a mixed-integer linear programming (MIP) problem. We extend this approach by leveraging automated algorithm configuration techniques and, more specifically, construct a portfolio of MIP solver configurations optimised for the neural network verification task. We test this approach on two recent, state-of-the-art MIP-based verification engines, MIPVerify and Venus, and achieve substantial improvements in CPU time by average factors of up to 4.7 and 10.3, respectively. |
| Author | König, Matthias Rijn, Jan N. van Hoos, Holger H. |
| Author_xml | – sequence: 1 givenname: Matthias surname: König fullname: König, Matthias email: h.m.t.konig@liacs.leidenuniv.nl organization: Leiden Institute of Advanced Computer Science, Leiden University – sequence: 2 givenname: Holger H. surname: Hoos fullname: Hoos, Holger H. organization: Leiden Institute of Advanced Computer Science, Leiden University, University of British Columbia – sequence: 3 givenname: Jan N. van surname: Rijn fullname: Rijn, Jan N. van organization: Leiden Institute of Advanced Computer Science, Leiden University |
| BookMark | eNp9kMtqHDEQRUWwwePHD3glyLoTPVpqeRlMHAcGsrCzFhqpuq1Jt9SR1J7kM_zH1swEDFl4IRVU3VO3uOfoJMQACF1T8okS0n3OlNzctA1hrCGSUdbsPqAVFR1viJDiBK2IUqKRlIkzdJ7zlhDCpJIr9PIwAzgfBrzMOMCSzFhL2cX0C6e4WXIJkDN-huR7b03xMeBnb7AZh5h8eZqwjaH3QwUPMxNcfTjOxU8-g8OT_1N_HwoMkPDoA5iE5xSHZKZp75vjWLfjOabSx9HHS3TamzHD1b96gX7efX28vW_WP759v_2ybiyXvDTUWdUpRl3XM6sU7zcbwdvalOCkAKpq0yhlO8aAOsU4VD1xjkgueqEsv0Afj3vrMb8XyEVv45JCtdSsaynlrWxlVbGjyqaYc4Jez8lPJv3VlOh99PoYva7R60P0elch9R9kfTnkU5Lx4_soP6K5-oQa2dtV71Cv6IugBw |
| CitedBy_id | crossref_primary_10_1007_s10462_024_10726_1 crossref_primary_10_1145_3673226 |
| Cites_doi | 10.1016/j.artint.2016.09.006 10.1109/DASC.2016.7778091 10.1007/978-3-642-23786-7_35 10.1007/978-3-319-77935-5_9 10.1609/aaai.v24i1.7565 10.1109/TEVC.2015.2474158 10.1007/978-3-319-50137-6_7 10.1109/FAMCAD.2007.9 10.1007/978-3-642-33558-7_38 10.1016/j.ejor.2013.10.043 10.1109/SP.2018.00058 10.1609/aaai.v29i1.9354 10.1609/socs.v4i1.18293 10.1109/TNNLS.2018.2808470 10.1609/aaai.v34i04.5729 10.1007/978-3-642-20895-9_40 10.1007/978-3-319-63387-9_5 10.1109/SP.2016.41 10.1109/SP.2017.49 10.1609/aaai.v32i1.11302 10.1007/978-3-319-68167-2_19 10.1007/s10601-018-9285-6 10.1145/2487575.2487629 10.1613/jair.2861 10.1007/978-3-642-13520-0_23 10.1109/SP.2019.00044 10.1023/A:1010933404324 10.1613/jair.4726 10.1007/978-3-642-25566-3_40 10.1007/978-3-319-68167-2_18 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2022 The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2022 – notice: The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION 3V. 7SC 7XB 88I 8AL 8AO 8FD 8FE 8FG 8FK ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO GNUQQ HCIFZ JQ2 K7- L7M L~C L~D M0N M2P P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PRINS Q9U |
| DOI | 10.1007/s10994-022-06212-w |
| DatabaseName | Springer Nature OA Free Journals CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts ProQuest Central (purchase pre-March 2016) Science Database (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Technology Collection ProQuest One Community College ProQuest Central ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic |
| DatabaseTitle | CrossRef Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Pharma Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace Advanced Technologies & Aerospace Collection ProQuest Computing ProQuest Science Journals (Alumni Edition) ProQuest Central Basic ProQuest Science Journals ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Academic ProQuest Central (Alumni) ProQuest One Academic (New) |
| DatabaseTitleList | CrossRef Computer Science Database |
| Database_xml | – sequence: 1 dbid: BENPR name: ProQuest Central (subscription) url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1573-0565 |
| EndPage | 4584 |
| ExternalDocumentID | 10_1007_s10994_022_06212_w |
| GrantInformation_xml | – fundername: TAILOR grantid: 952215 |
| GroupedDBID | -4Z -59 -5G -BR -EM -Y2 -~C -~X .4S .86 .DC .VR 06D 0R~ 0VY 199 1N0 1SB 2.D 203 28- 29M 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6NX 6TJ 78A 88I 8AO 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAEWM AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABIVO ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACGOD ACHSB ACHXU ACKNC ACMDZ ACMLO ACNCT ACOKC ACOMO ACPIV ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BPHCQ BSONS C6C CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EBLON EBS EIOEI EJD ESBYG F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITG ITH ITM IWAJR IXC IZIGR IZQ I~X I~Y I~Z J-C J0Z JBSCW JCJTX JZLTJ K6V K7- KDC KOV KOW LAK LLZTM M0N M2P M4Y MA- MVM N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P62 P9O PF- PQQKQ PROAC PT4 Q2X QF4 QM1 QN7 QO4 QOK QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV RZC RZE S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TAE TEORI TN5 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW VXZ W23 W48 WH7 WIP WK8 XJT YLTOR Z45 Z7R Z7S Z7U Z7V Z7W Z7X Z7Y Z7Z Z81 Z83 Z85 Z86 Z87 Z88 Z8M Z8N Z8O Z8P Z8Q Z8R Z8S Z8T Z8U Z8W Z8Z Z91 Z92 ZMTXR AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG ADKFA AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP AMVHM ATHPR AYFIA CITATION PHGZM PHGZT PQGLB 7SC 7XB 8AL 8FD 8FK JQ2 L7M L~C L~D PKEHL PQEST PQUKI PRINS Q9U |
| ID | FETCH-LOGICAL-c363t-1dc87821d7f2c883fbb534dc86ed65e18c88a88c722e1d823ec870dd0635f58c3 |
| IEDL.DBID | RSV |
| ISICitedReferencesCount | 5 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000851348600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0885-6125 |
| IngestDate | Wed Nov 05 04:18:09 EST 2025 Sat Nov 29 01:43:29 EST 2025 Tue Nov 18 22:52:09 EST 2025 Fri Feb 21 02:44:11 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 12 |
| Keywords | Algorithm selection Mixed integer programming Neural network verification Automated algorithm configuration |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c363t-1dc87821d7f2c883fbb534dc86ed65e18c88a88c722e1d823ec870dd0635f58c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| OpenAccessLink | https://link.springer.com/10.1007/s10994-022-06212-w |
| PQID | 2741134646 |
| PQPubID | 54194 |
| PageCount | 20 |
| ParticipantIDs | proquest_journals_2741134646 crossref_primary_10_1007_s10994_022_06212_w crossref_citationtrail_10_1007_s10994_022_06212_w springer_journals_10_1007_s10994_022_06212_w |
| PublicationCentury | 2000 |
| PublicationDate | 20221200 2022-12-00 20221201 |
| PublicationDateYYYYMMDD | 2022-12-01 |
| PublicationDate_xml | – month: 12 year: 2022 text: 20221200 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York – name: Dordrecht |
| PublicationTitle | Machine learning |
| PublicationTitleAbbrev | Mach Learn |
| PublicationYear | 2022 |
| Publisher | Springer US Springer Nature B.V |
| Publisher_xml | – name: Springer US – name: Springer Nature B.V |
| References | Kashgarani, H., & Kotthoff, L. (2021). Is algorithm selection worth it? Comparing selecting single algorithms and parallel execution. In AAAI Workshop on Meta-Learning and MetaDL Challenge, pp. 58–64. HutterFLindauerMBalintABaylessSHoosHLeyton-BrownKThe configurable SAT solver challenge (CSSC)Artificial Intelligence2017243125358214210.1016/j.artint.2016.09.0061402.68161 Hutter, F., Hoos, H. H., Leyton-Brown, K. (2011). Sequential model-based optimization for general algorithm configuration. In Proceedings of the 5th International Conference on Learning and Intelligent Optimization (LION 5), pp. 507–523 Raghunathan, A., Steinhardt, J., & Liang, P. (2018). Certified defenses against adversarial examples. arXiv preprint arXiv:1801.09344 Malitsky, Y., Sabharwal, A., Samulowitz, H., & Sellmann, M. (2012). Parallel SAT Solver Selection and Scheduling. In Proceedings of the Eighteenth International Conference on Principles and Practice of Constraint Programming (CP2012), pp. 512–526 Tjeng, V., Xiao, .K, & Tedrake, R. (2019). Evaluating robustness of neural networks with mixed integer programming. In Proceedings of the 7th International Conference on Learning Representations (ICLR 2019) Wong, E., & Kolter, Z. (2018.) Provable defenses against adversarial examples via the convex outer adversarial polytope. In Proceedings of The Thirty-Fifth International Conference on Machine Learning (ICML2018), pp 5286–5295. BezerraLCLópez-IbánezMStützleTAutomatic component-wise design of multiobjective evolutionary algorithmsIEEE Transactions on Evolutionary Computation201520340341710.1109/TEVC.2015.2474158 HutterFHoosHHLeyton-BrownKStützleTParamILS: An automatic algorithm configuration frameworkJournal of Artificial Intelligence Research20093626730610.1613/jair.28611192.68831 Carlini, N., & Wagner, D. (2017). Towards evaluating the robustness of neural networks. In Proceedings of the 38th IEEE Symposium on Security and Privacy (IEEE S &P 2017), pp. 39–57 Katz, G., Barrett, C., Dill, D. L., Julian, K., & Kochenderfer, M. J. (2017). Reluplex: An efficient SMT solver for verifying deep neural networks. In Proceedings of the 29th International Conference on Computer Aided Verification(CAV 2017), pp. 97–117 Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., \& Fergus, R. (2014). Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 Dvijotham, K., Stanforth, R., Gowal, S., Mann, T. A., & Kohli, P. (2018). A Dual Approach to Scalable Verification of Deep Networks. In Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2018), pp. 550–559. Chiarandini, M., Fawcett, C., & Hoos, H. H. (2008). A Modular Multiphase Heuristic Solver for Post Enrolment Course Timetabling. In Proceedings of the 7th International Conference on the Practice and Theory of Automated Timetabling (PATAT 2008). Kurakin, A., Goodfellow, I., & Bengio, S. (2016). Adversarial examples in the physical world. arXiv preprint arXiv:1607.02533 Hutter, F., Babic, D., Hoos, H. H., & Hu, A. J. (2007). Boosting verification by automatic tuning of decision procedures. In Formal Methods in Computer Aided Design (FMCAD’07), pp. 27–34 Mohapatra, J., Ko, C. Y., Weng, L., Chen, P. Y., Liu, S., & Daniel, L. (2021). Hidden cost of randomized smoothing. In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (AISTATS2021), pp 4033–4041. XiangWTranHDJohnsonTTOutput Reachable Set Estimation and Verification for Multilayer Neural NetworksIEEE Transactions on Neural Networks and Learning Systems2018291157775783386788510.1109/TNNLS.2018.2808470 Ehlers, R. (2017). Formal verification of piece-wise linear feed-forward neural networks. In Proceedings of the 15th International Symposium on Automated Technology for Verification and Analysis (ATVA 2017), pp. 269–286. Lopez-IbanezMStützleTAutomatically improving the anytime behaviour of optimisation algorithmsEuropean Journal of Operational Research20142353569582316615310.1016/j.ejor.2013.10.0431401.90274 FischettiMJoJDeep neural networks and mixed integer linear optimizationConstraints2018233296309381467210.1007/s10601-018-9285-61402.90096 Thornton, C., Hutter, F., Hoos, H. H., \& Leyton-Brown, K. (2013). Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2013), pp. 847–855 Bastani, O., Ioannou, Y., Lampropoulos, L., Vytiniotis, D., Nori, A., & Criminisi, A. (2016). Measuring neural net robustness with constraints. In Proceedings of the 30th Conference on Neural Information Processing Systems (NeurIPS 2016), pp 2613–2621 Papernot, N., McDaniel, P., Wu, X., Jha, S., & Swami, A. (2016). Distillation as a defense to adversarial perturbations against deep neural networks. In Proceedings of the 37th IEEE Symposium on Security and Privacy (IEEE S &P 2016), pp. 582–597. Xu, L., Hutter, F., Hoos, H. H., Leyton-Brown, K. (2011). Hydra-MIP: Automated algorithm configuration and selection for mixed integer programming. In RCRA Workshop on Experimental evaluation of Algorithms for Solving Problems with Combinatorial Explosion, pp. 16–30 Akintunde, M., Lomuscio, A., Maganti, L., & Pirovano, E. (2018) Reachability analysis for neural agent-environment systems. In Proceedings of The Sixteenth International Conference on Principles of Knowledge Representation and Reasoning (KR2018) Cheng, C. H., Nührenberg, G., & Ruess , H. (2017). Maximum resilience of artificial neural networks. In Proceedings of The 15th International Symposium on Automated Technology for Verification and Analysis (ATVA2017), pp. 251–268. Lecuyer, M., Atlidakis, V., Geambasu, R., Hsu, D., & Jana S (2019) Certified robustness to adversarial examples with differential privacy. In Proceedings of The Fortieth IEEE Symposium on Security and Privacy (SP2019), IEEE, pp 656–672. Botoeva, E., Kouvaros, P., Kronqvist, J., Lomuscio, A., & Misener, R. (2020). Efficient verification of ReLU-based neural networks via dependency analysis. In Proceedings of The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI20) (pp. 3291–3299) Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2010). Automated Configuration of Mixed Integer Programming Solvers. In Proceedings of the 7th International Conference on Integration of Artificial Intelligence (AI) and Operations Research (OR) Techniques in Constraint Programming (CPAIOR 2010), pp. 186–202 Carlini, N., Katz, G., Barrett, C., & Dill, D. L. (2017) Provably Minimally-Distorted Adversarial Examples. arXiv preprint arXiv:1709.10207 Lomuscio, A., & Maganti, L. (2017). An approach to reachability analysis for feed-forward ReLU neural networks. arXiv preprint arXiv:1706.07351 Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T., Schneider, M. T., & Ziller, S. (2011). A portfolio solver for answer set programming: Preliminary report. In Proceedings of The Tenth International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR2019), pp. 352–357. Dutta, S., Jha, S., Sankaranarayanan, S., & Tiwari, A. (2018) Output range analysis for deep neural networks. In Proceedings of The Tenth NASA Formal Methods Symposium (NFM 2018), pp. 121–138. Kotthoff, L. (2016). Algorithm selection for combinatorial search problems: A survey. In Data Mining and Constraint Programming. Springer, pp. 149–190. Julian, K. D., Lopez, J., Brush, J. S., Owen, M. P., & Kochenderfer, M. J. (2016). Policy compression for aircraft collision avoidance systems. In Proceedings of the Thirty-Fifth Digital Avionics Systems Conference (DASC2016), pp. 1–10 König, M., Hoos, H. H., van Rijn, J. N. (2021). Speeding up neural network verification via automated algorithm configuration. In ICLR Workshop on Security and Safety in Machine Learning Systems. Gehr, T., Mirman, M., Drachsler-Cohen, D., Tsankov, P., Chaudhuri, S., & Vechev, M. (2018). AI2: Safety and robustness certification of neural networks with abstract interpretation. In Proceedings of the 39th IEEE Symposium on Security and Privacy (IEEE S &P 2018), pp. 3–18. Kadioglu, S., Malitsky, Y., Sabharwal, A., Samulowitz, H., & Sellmann, M. (2011). Algorithm selection and scheduling. In Proceedings of the Seventeenth International Conference on Principles and Practice of Constraint Programming (CP2011), pp. 454–469 Feurer, M., Springenberg, J. T., & Hutter, F. (2015). Initializing Bayesian hyperparameter optimization via meta-learning. In Proceedings of The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI15) Bunel, R .R ., Turkaslan, I., Torr, P., Kohli, P., & Mudigonda, P. K. (2018). A unified view of piecewise linear neural network verification. In Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), pp. 4790–4799 Cohen, J., Rosenfeld, E., & Kolter, Z. (2019). Certified adversarial robustness via randomized smoothing. In Proceedings of the Thirty-Sixth International Conference on Machine Learning (ICML2019), pp 1310–1320. LindauerMHoosHHHutterFSchaubTAutoFolio: An automatically configured algorithm selectorJournal of Artificial Intelligence Research201553745778340338710.1613/jair.4726 Vallati, M., Fawcett, C., Gerevini, A. E., Hoos, H., \& Saetti, A. (2013). Automatic generation of efficient domain-specific planners from generic parametrized planners. In Proceedings of the 6th Annual Symposium on Combinatorial Search (SOCS), pp. 184–192. BreimanLRandom forestsMachine Learning200145153210.1023/A:10109334043241007.68152 Chen, P. Y., Sharma, Y., Zhang, H., Yi, J., & Hsieh, C. J. (2018). Ead: Elastic-net attacks to deep neural networks via adversarial examples. In Proceedings of The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI18) Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 Xu L, Hoos H, Leyton-Brown K (2010) Hydra: Automatically Configuring Algorithms for Portfolio-Based Selection. In: P 6212_CR30 6212_CR31 F Hutter (6212_CR22) 2009; 36 M Lopez-Ibanez (6212_CR36) 2014; 235 LC Bezerra (6212_CR3) 2015; 20 6212_CR32 6212_CR33 M Lindauer (6212_CR34) 2015; 53 6212_CR35 6212_CR37 6212_CR38 6212_CR39 6212_CR20 6212_CR29 6212_CR21 6212_CR23 6212_CR24 6212_CR26 6212_CR27 6212_CR28 6212_CR2 6212_CR1 6212_CR4 6212_CR6 6212_CR8 6212_CR7 6212_CR9 F Hutter (6212_CR25) 2017; 243 L Breiman (6212_CR5) 2001; 45 6212_CR18 6212_CR19 6212_CR10 6212_CR11 6212_CR12 6212_CR13 6212_CR14 6212_CR15 W Xiang (6212_CR47) 2018; 29 6212_CR16 6212_CR40 6212_CR41 6212_CR42 M Fischetti (6212_CR17) 2018; 23 6212_CR43 6212_CR44 6212_CR45 6212_CR46 6212_CR48 6212_CR49 |
| References_xml | – reference: Cohen, J., Rosenfeld, E., & Kolter, Z. (2019). Certified adversarial robustness via randomized smoothing. In Proceedings of the Thirty-Sixth International Conference on Machine Learning (ICML2019), pp 1310–1320. – reference: Xu, L., Hutter, F., Hoos, H. H., Leyton-Brown, K. (2011). Hydra-MIP: Automated algorithm configuration and selection for mixed integer programming. In RCRA Workshop on Experimental evaluation of Algorithms for Solving Problems with Combinatorial Explosion, pp. 16–30 – reference: Dutta, S., Jha, S., Sankaranarayanan, S., & Tiwari, A. (2018) Output range analysis for deep neural networks. In Proceedings of The Tenth NASA Formal Methods Symposium (NFM 2018), pp. 121–138. – reference: Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2010). Automated Configuration of Mixed Integer Programming Solvers. In Proceedings of the 7th International Conference on Integration of Artificial Intelligence (AI) and Operations Research (OR) Techniques in Constraint Programming (CPAIOR 2010), pp. 186–202 – reference: XiangWTranHDJohnsonTTOutput Reachable Set Estimation and Verification for Multilayer Neural NetworksIEEE Transactions on Neural Networks and Learning Systems2018291157775783386788510.1109/TNNLS.2018.2808470 – reference: Bunel, R .R ., Turkaslan, I., Torr, P., Kohli, P., & Mudigonda, P. K. (2018). A unified view of piecewise linear neural network verification. In Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), pp. 4790–4799 – reference: Carlini, N., & Wagner, D. (2017). Towards evaluating the robustness of neural networks. In Proceedings of the 38th IEEE Symposium on Security and Privacy (IEEE S &P 2017), pp. 39–57 – reference: Gehr, T., Mirman, M., Drachsler-Cohen, D., Tsankov, P., Chaudhuri, S., & Vechev, M. (2018). AI2: Safety and robustness certification of neural networks with abstract interpretation. In Proceedings of the 39th IEEE Symposium on Security and Privacy (IEEE S &P 2018), pp. 3–18. – reference: Malitsky, Y., Sabharwal, A., Samulowitz, H., & Sellmann, M. (2012). Parallel SAT Solver Selection and Scheduling. In Proceedings of the Eighteenth International Conference on Principles and Practice of Constraint Programming (CP2012), pp. 512–526 – reference: Wong, E., & Kolter, Z. (2018.) Provable defenses against adversarial examples via the convex outer adversarial polytope. In Proceedings of The Thirty-Fifth International Conference on Machine Learning (ICML2018), pp 5286–5295. – reference: Cheng, C. H., Nührenberg, G., & Ruess , H. (2017). Maximum resilience of artificial neural networks. In Proceedings of The 15th International Symposium on Automated Technology for Verification and Analysis (ATVA2017), pp. 251–268. – reference: BreimanLRandom forestsMachine Learning200145153210.1023/A:10109334043241007.68152 – reference: Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., \& Fergus, R. (2014). Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 – reference: Scheibler, K., Winterer, L., Wimmer, R., & Becker, B. (2015). Towards verification of artificial neural networks. In Proceedings of the 18th Workshop on Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen (MBMV 2015), pp. 30–40. – reference: Chen, P. Y., Sharma, Y., Zhang, H., Yi, J., & Hsieh, C. J. (2018). Ead: Elastic-net attacks to deep neural networks via adversarial examples. In Proceedings of The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI18) – reference: Chiarandini, M., Fawcett, C., & Hoos, H. H. (2008). A Modular Multiphase Heuristic Solver for Post Enrolment Course Timetabling. In Proceedings of the 7th International Conference on the Practice and Theory of Automated Timetabling (PATAT 2008). – reference: Carlini, N., Katz, G., Barrett, C., & Dill, D. L. (2017) Provably Minimally-Distorted Adversarial Examples. arXiv preprint arXiv:1709.10207 – reference: Lecuyer, M., Atlidakis, V., Geambasu, R., Hsu, D., & Jana S (2019) Certified robustness to adversarial examples with differential privacy. In Proceedings of The Fortieth IEEE Symposium on Security and Privacy (SP2019), IEEE, pp 656–672. – reference: Lomuscio, A., & Maganti, L. (2017). An approach to reachability analysis for feed-forward ReLU neural networks. arXiv preprint arXiv:1706.07351 – reference: Dvijotham, K., Stanforth, R., Gowal, S., Mann, T. A., & Kohli, P. (2018). A Dual Approach to Scalable Verification of Deep Networks. In Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2018), pp. 550–559. – reference: Thornton, C., Hutter, F., Hoos, H. H., \& Leyton-Brown, K. (2013). Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2013), pp. 847–855 – reference: Kotthoff, L. (2016). Algorithm selection for combinatorial search problems: A survey. In Data Mining and Constraint Programming. Springer, pp. 149–190. – reference: Vallati, M., Fawcett, C., Gerevini, A. E., Hoos, H., \& Saetti, A. (2013). Automatic generation of efficient domain-specific planners from generic parametrized planners. In Proceedings of the 6th Annual Symposium on Combinatorial Search (SOCS), pp. 184–192. – reference: Ehlers, R. (2017). Formal verification of piece-wise linear feed-forward neural networks. In Proceedings of the 15th International Symposium on Automated Technology for Verification and Analysis (ATVA 2017), pp. 269–286. – reference: Lopez-IbanezMStützleTAutomatically improving the anytime behaviour of optimisation algorithmsEuropean Journal of Operational Research20142353569582316615310.1016/j.ejor.2013.10.0431401.90274 – reference: LindauerMHoosHHHutterFSchaubTAutoFolio: An automatically configured algorithm selectorJournal of Artificial Intelligence Research201553745778340338710.1613/jair.4726 – reference: Kurakin, A., Goodfellow, I., & Bengio, S. (2016). Adversarial examples in the physical world. arXiv preprint arXiv:1607.02533 – reference: Papernot, N., McDaniel, P., Wu, X., Jha, S., & Swami, A. (2016). Distillation as a defense to adversarial perturbations against deep neural networks. In Proceedings of the 37th IEEE Symposium on Security and Privacy (IEEE S &P 2016), pp. 582–597. – reference: Feurer, M., Springenberg, J. T., & Hutter, F. (2015). Initializing Bayesian hyperparameter optimization via meta-learning. In Proceedings of The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI15) – reference: Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T., Schneider, M. T., & Ziller, S. (2011). A portfolio solver for answer set programming: Preliminary report. In Proceedings of The Tenth International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR2019), pp. 352–357. – reference: HutterFHoosHHLeyton-BrownKStützleTParamILS: An automatic algorithm configuration frameworkJournal of Artificial Intelligence Research20093626730610.1613/jair.28611192.68831 – reference: Hutter, F., Hoos, H. H., Leyton-Brown, K. (2011). Sequential model-based optimization for general algorithm configuration. In Proceedings of the 5th International Conference on Learning and Intelligent Optimization (LION 5), pp. 507–523 – reference: BezerraLCLópez-IbánezMStützleTAutomatic component-wise design of multiobjective evolutionary algorithmsIEEE Transactions on Evolutionary Computation201520340341710.1109/TEVC.2015.2474158 – reference: FischettiMJoJDeep neural networks and mixed integer linear optimizationConstraints2018233296309381467210.1007/s10601-018-9285-61402.90096 – reference: König, M., Hoos, H. H., van Rijn, J. N. (2021). Speeding up neural network verification via automated algorithm configuration. In ICLR Workshop on Security and Safety in Machine Learning Systems. – reference: Akintunde, M., Lomuscio, A., Maganti, L., & Pirovano, E. (2018) Reachability analysis for neural agent-environment systems. In Proceedings of The Sixteenth International Conference on Principles of Knowledge Representation and Reasoning (KR2018) – reference: Botoeva, E., Kouvaros, P., Kronqvist, J., Lomuscio, A., & Misener, R. (2020). Efficient verification of ReLU-based neural networks via dependency analysis. In Proceedings of The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI20) (pp. 3291–3299) – reference: Julian, K. D., Lopez, J., Brush, J. S., Owen, M. P., & Kochenderfer, M. J. (2016). Policy compression for aircraft collision avoidance systems. In Proceedings of the Thirty-Fifth Digital Avionics Systems Conference (DASC2016), pp. 1–10 – reference: Mohapatra, J., Ko, C. Y., Weng, L., Chen, P. Y., Liu, S., & Daniel, L. (2021). Hidden cost of randomized smoothing. In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (AISTATS2021), pp 4033–4041. – reference: Kadioglu, S., Malitsky, Y., Sabharwal, A., Samulowitz, H., & Sellmann, M. (2011). Algorithm selection and scheduling. In Proceedings of the Seventeenth International Conference on Principles and Practice of Constraint Programming (CP2011), pp. 454–469 – reference: Katz, G., Barrett, C., Dill, D. L., Julian, K., & Kochenderfer, M. J. (2017). Reluplex: An efficient SMT solver for verifying deep neural networks. In Proceedings of the 29th International Conference on Computer Aided Verification(CAV 2017), pp. 97–117 – reference: Raghunathan, A., Steinhardt, J., & Liang, P. (2018). Certified defenses against adversarial examples. arXiv preprint arXiv:1801.09344 – reference: HutterFLindauerMBalintABaylessSHoosHLeyton-BrownKThe configurable SAT solver challenge (CSSC)Artificial Intelligence2017243125358214210.1016/j.artint.2016.09.0061402.68161 – reference: Xu L, Hoos H, Leyton-Brown K (2010) Hydra: Automatically Configuring Algorithms for Portfolio-Based Selection. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI10) – reference: Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 – reference: Kashgarani, H., & Kotthoff, L. (2021). Is algorithm selection worth it? Comparing selecting single algorithms and parallel execution. In AAAI Workshop on Meta-Learning and MetaDL Challenge, pp. 58–64. – reference: Tjeng, V., Xiao, .K, & Tedrake, R. (2019). Evaluating robustness of neural networks with mixed integer programming. In Proceedings of the 7th International Conference on Learning Representations (ICLR 2019) – reference: Hutter, F., Babic, D., Hoos, H. H., & Hu, A. J. (2007). Boosting verification by automatic tuning of decision procedures. In Formal Methods in Computer Aided Design (FMCAD’07), pp. 27–34 – reference: Bastani, O., Ioannou, Y., Lampropoulos, L., Vytiniotis, D., Nori, A., & Criminisi, A. (2016). Measuring neural net robustness with constraints. In Proceedings of the 30th Conference on Neural Information Processing Systems (NeurIPS 2016), pp 2613–2621 – volume: 243 start-page: 1 year: 2017 ident: 6212_CR25 publication-title: Artificial Intelligence doi: 10.1016/j.artint.2016.09.006 – ident: 6212_CR42 – ident: 6212_CR26 doi: 10.1109/DASC.2016.7778091 – ident: 6212_CR27 doi: 10.1007/978-3-642-23786-7_35 – ident: 6212_CR46 – ident: 6212_CR13 doi: 10.1007/978-3-319-77935-5_9 – ident: 6212_CR48 doi: 10.1609/aaai.v24i1.7565 – volume: 20 start-page: 403 issue: 3 year: 2015 ident: 6212_CR3 publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2015.2474158 – ident: 6212_CR49 – ident: 6212_CR31 doi: 10.1007/978-3-319-50137-6_7 – ident: 6212_CR6 – ident: 6212_CR2 – ident: 6212_CR14 – ident: 6212_CR41 – ident: 6212_CR21 doi: 10.1109/FAMCAD.2007.9 – ident: 6212_CR20 – ident: 6212_CR37 doi: 10.1007/978-3-642-33558-7_38 – volume: 235 start-page: 569 issue: 3 year: 2014 ident: 6212_CR36 publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2013.10.043 – ident: 6212_CR19 doi: 10.1109/SP.2018.00058 – ident: 6212_CR16 doi: 10.1609/aaai.v29i1.9354 – ident: 6212_CR45 doi: 10.1609/socs.v4i1.18293 – ident: 6212_CR1 – ident: 6212_CR30 – volume: 29 start-page: 5777 issue: 11 year: 2018 ident: 6212_CR47 publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2018.2808470 – ident: 6212_CR4 doi: 10.1609/aaai.v34i04.5729 – ident: 6212_CR38 – ident: 6212_CR18 doi: 10.1007/978-3-642-20895-9_40 – ident: 6212_CR29 doi: 10.1007/978-3-319-63387-9_5 – ident: 6212_CR39 doi: 10.1109/SP.2016.41 – ident: 6212_CR7 doi: 10.1109/SP.2017.49 – ident: 6212_CR9 doi: 10.1609/aaai.v32i1.11302 – ident: 6212_CR15 doi: 10.1007/978-3-319-68167-2_19 – ident: 6212_CR44 – ident: 6212_CR40 – ident: 6212_CR8 – ident: 6212_CR28 – volume: 23 start-page: 296 issue: 3 year: 2018 ident: 6212_CR17 publication-title: Constraints doi: 10.1007/s10601-018-9285-6 – ident: 6212_CR35 – ident: 6212_CR43 doi: 10.1145/2487575.2487629 – volume: 36 start-page: 267 year: 2009 ident: 6212_CR22 publication-title: Journal of Artificial Intelligence Research doi: 10.1613/jair.2861 – ident: 6212_CR23 doi: 10.1007/978-3-642-13520-0_23 – ident: 6212_CR12 – ident: 6212_CR33 doi: 10.1109/SP.2019.00044 – volume: 45 start-page: 5 issue: 1 year: 2001 ident: 6212_CR5 publication-title: Machine Learning doi: 10.1023/A:1010933404324 – volume: 53 start-page: 745 year: 2015 ident: 6212_CR34 publication-title: Journal of Artificial Intelligence Research doi: 10.1613/jair.4726 – ident: 6212_CR24 doi: 10.1007/978-3-642-25566-3_40 – ident: 6212_CR10 doi: 10.1007/978-3-319-68167-2_18 – ident: 6212_CR11 – ident: 6212_CR32 |
| SSID | ssj0002686 |
| Score | 2.4372861 |
| Snippet | Despite their great success in recent years, neural networks have been found to be vulnerable to adversarial attacks. These attacks are often based on slight... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 4565 |
| SubjectTerms | Algorithms Artificial Intelligence Automation Computer Science Configurations Control Engines Integer programming Linear programming Machine Learning Mechatronics Mixed integer Natural Language Processing (NLP) Neural networks Perturbation Robotics Robustness Simulation and Modeling Solvers Special Issue of the ECML PKDD 2022 Journal Track Verification |
| SummonAdditionalLinks | – databaseName: Computer Science Database dbid: K7- link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8QwEA66evDiW1xdJQdvWtykr_QkIoqgiKCCt9LmoYXddt3tqn_Df-xMmroo6MVDe0jzKMw3r2QyQ8iB0HEiEpUAiytwUEDneHkmhZeHsTLCD6Qw9qLwdXxzIx4fk1u34TZxYZWtTLSCWlUS98iPMc0K84MoiE5GLx5WjcLTVVdCY54sMM4Z4vwq9r4kMY9spUdgpNBDTe4uzbirczYpLrhi_YhjdY_vimlmbf44ILV652Llv3-8SpadxUlPG4iskTldrpOVtpoDdcy9QT7uRo0mo9MRxTSXMKhsgsTpuMqnkxqlIgXoY3SRJSh9LTKaDZ5g1fp5SMG1NsXTtMEUzUoFD61AJgGWtKLD4h3eNj8FrIvmbTamLj5siOsCG8DsFD0CUw2KapM8XJzfn116rmCDJ_3Irz2mpACLg6nYcCmEb_I89ANojLSKQs0ENGZCyJhzzZTgvob-faXATApNKKS_RTplVeptQo0UKsfuSd8EfQH4YSAOlQy1TsCtzrqEtdRKpctmjkU1BuksDzNSOAUKp5bC6VuXHH6NGTW5PP7s3WvJmjq-nqQzmnbJUQuM2effZ9v5e7ZdssQRizZOpkc69Xiq98iifK2LyXjfovoTLnUB3A priority: 102 providerName: ProQuest |
| Title | Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio |
| URI | https://link.springer.com/article/10.1007/s10994-022-06212-w https://www.proquest.com/docview/2741134646 |
| Volume | 111 |
| WOSCitedRecordID | wos000851348600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1573-0565 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002686 issn: 0885-6125 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR3JSuRA9OF28OIu9ow2dfCmgc7-cnREEUabxg3xEpJaNNCdNOm042_MH8-rSmKrjIIeUofkVVWot1NvAdhHGUYYiYhYXJCDQjrHShOOVuqHQqHrcVQmUfg87Pfx7i4aNElhkzbavb2SNJL6VbKbKWNLzlMvcHQ_jnlYJHWHmh0vr25f5K8TmP6OxD6-pfV3kyrz_zXeqqOZjfnuWtRom9PV7_3nGqw01iU7qslhHeZkvgGrbecG1jDyJvy9Gtdai03HTJe0pEl5HRDOyiKdTiotARmRuY4kMshjT1nCkuFDUWbV44iRG62yh2lNPyzJBT2sIPlDdCMFG2XPNJpaFLSvNmWTkjWxYCO9L5E8rc609a-KYVZswc3pyfXxmdU0Z7C4G7iVZQuOZF3YIlQOR3RVmvquRy8DKQJf2kgvE0QeOo60BTquJPieEGQS-cpH7m7DQl7kcgeY4ihSDR71lNdDohWbRJ_gvpQRudBJB-wWRzFvKpfrBhrDeFZzWZ95TGcemzOP_3Tg4GXOuK7b8Sn0bov6uOHhSawL-9iuF3hBBw5bVM8-f7zaj6-B_4RlR1OLiZHZhYWqnMo9WOJPVTYpu7D466Q_uOzC_O_QovHCGdA48O-7hur_ARn4_T4 |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT9wwEB5RWqlcSl-ILZT60J7aqBs7D-dQIURBILYrpFIJ9ZImfkCk3WTZzQL9Gf0j_Y3MOAmrVio3Dj0kB8cPxfm-GU88ngF4K02cyEQnSHGNBgrqHC_PlPTyMNZWikBJ6w4KD-LhUJ6eJsdL8Ls7C0NulZ1MdIJaV4r-kX-kMCu-CKIg2p5ceJQ1inZXuxQaDSyOzM8rNNlmnw4_4_d9x_n-3snugddmFfCUiETt-VpJVIu-ji1XUgqb56EIsDAyOgqNL7Ewk1LFnBtfSy4M1u9rjbo8tKFUAvt9AA-DAI0l5M9x-P1W8vPIZZZE4oYerRzaQzrtUT0XhBdNv37EKZvIn4pwsbr9a0PW6bn91f9thp7Ck3ZFzXYaCjyDJVM-h9UuWwVrhdcL-PV10mhqNp8wCuOJjcrGCZ5Nq3w-q0nqM6Q2eU85wLLLImPZ6Azfsj4fM1WVtjibN5xhWanxYhXKXOSK0WxcXOPdxd_AcWn5nk1Z6_82pnGR5tg7I4vHVqOiegnf7mVi1mC5rEqzDswqqXOqnvRt0JfIDx_FvVahMYkvRNYDv0NHqtpo7ZQ0ZJQu4kwTolJEVOoQlV714P1tm0kTq-TO2psdjNJWbs3SBYZ68KED4uLxv3t7dXdvb-DxwcmXQTo4HB5twAonHjifoE1Yrqdz8xoeqcu6mE23HKMY_LhvgN4A_sRfNA |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VglAvlKe60IIPcIKoGzsP54AQallRtVqtBEgVl5D4USLtJstutoWfwd_h1zHjOF2BRG89cEgOiWMryffN50nGMwDPpUkzmekMKa7RQUHNCcpCyaCMU22liJS0bqHwSToey9PTbLIBv_q1MBRW2dtEZ6h1o-gb-T6lWQlFlETJvvVhEZPD0Zv5t4AqSNGf1r6cRgeRY_PjAt235eujQ3zXLzgfvft48D7wFQYCJRLRBqFWEiUy1KnlSkphyzIWER5MjE5iE0o8WEipUs5NqCUXBtsPtUZdj20slcB-b8DNFH1Mcvwm8edLFeCJqzKJJI4DmkX4BTt-2Z5LyItu4DDhVFnkT1Fcz3T_-jnrNG-0_T8_rbtwx8-02duOGvdgw9T3YbuvYsG8UXsAPz_MOwVnqzmj9J54Ud0Fx7NFU66WLakBQ8pTVJUDMjuvClZMz_Au268zppraVmerjkusqDVurEFbjBwyms2q77h3eTlwXJrWFwvm4-JmNC7SH3tn5AnZZlo1D-HTtTyYR7BZN7XZAWaV1CU1z4Y2GkrkTYgyoFVsTBYKUQwg7JGSK5_FnYqJTPN1_mlCV47oyh268osBvLy8Zt7lMLmy9W4Pqdzbs2W-xtMAXvWgXJ_-d2-Pr-7tGdxGXOYnR-PjJ7DFiRIuVGgXNtvFyuzBLXXeVsvFU0cuBl-uG5-_AVvgaAg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Speeding+up+neural+network+robustness+verification+via+algorithm+configuration+and+an+optimised+mixed+integer+linear+programming+solver+portfolio&rft.jtitle=Machine+learning&rft.au=K%C3%B6nig%2C+Matthias&rft.au=Hoos%2C+Holger+H.&rft.au=Rijn%2C+Jan+N.+van&rft.date=2022-12-01&rft.issn=0885-6125&rft.eissn=1573-0565&rft.volume=111&rft.issue=12&rft.spage=4565&rft.epage=4584&rft_id=info:doi/10.1007%2Fs10994-022-06212-w&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s10994_022_06212_w |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0885-6125&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0885-6125&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0885-6125&client=summon |